Modelo de detección de textos generados con inteligencia artificial y generación de propuestas de retroalimentación pedagógica

Mario Alberto Camarillo Cabrera

doi:10.5281/zenodo.23127130

Authors

Mario Alberto Camarillo Cabrera Tecnologico de Estudios Superiores del Oriente del Estado de México https://orcid.org/0000-0003-3904-5401

DOI:

https://doi.org/10.5281/zenodo.23127130

Keywords:

Inteligencia artificialeducación detección retroalimentación escritura académica, detección de textos, retroalimentación formativa, escritura académica, procesamiento del lenguaje natural

Abstract

The irruption of generative artificial intelligence poses a complex challenge in higher education, making it difficult to distinguish between authentic productions and automatically generated texts. Current detection tools present significant flaws when analyzing Spanish texts and rely on a punitive approach that omits student training. This article proposes the development of an automated detection model for large language models that integrates the generation of pedagogical feedback proposals. Through an integrative approach, natural language processing and stylometry techniques are applied with formative assessment strategies based on socio-constructivism. The objective is to transform simple algorithmic detection into a resource that guides the student, allowing to reduce false positive rates and strengthen writing and critical thinking skills. It is concluded that addressing technological dependence from teaching, and not just from surveillance, promotes authentic authorship and academic integrity.

Author Biography

Mario Alberto Camarillo Cabrera, Tecnologico de Estudios Superiores del Oriente del Estado de México

Estudiante de la Mestría en Ingeniería en Sistemas Computacionales

References

Bender, E. M., Gebru, T., McMillan-Major, A., Shmitchell, S., (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610-623. DOI: 10.1145/3442188.3445922

Boud, D., Molloy, E., (2013). Rethinking models of feedback for learning: The challenge of design. Assessment & Evaluation in Higher Education 38(6), 698-712. DOI: 10.1080/02602938.2012.691462

Cotton, D. R. E., Cotton, P. A., Shipway, J. R., (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International 61(2), 228-239. DOI: 10.1080/14703297.2023.2190148

Elkhatat, A. M., Elsaid, K., Almeer, S., (2023). Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. International Journal for Educational Integrity 19, 17. DOI: 10.1007/s40979-023-00140-5

Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., Pearson, A. T., (2023). Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. npj Digital Medicine 6, 75. DOI: 10.1038/s41746-023-00819-6

Hattie, J., Timperley, H., (2007). The power of feedback. Review of Educational Research 77(1), 81-112. DOI: 10.3102/003465430298487

Hyland, K., Hyland, F., (2006). Feedback on second language students’ writing. Language Teaching 39(2), 83-101. DOI: 10.1017/S0261444806003399

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., Kasneci, G., (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences 103, 102274. DOI: 10.1016/j.lindif.2023.102274

Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., Zou, J., (2023). GPT detectors are biased against non-native English writers. Patterns 4(7), 100779. DOI: 10.1016/j.patter.2023.100779

Miao, F., Holmes, W., (2023). Guidance for generative AI in education and research. UNESCO.

Mitchell, E., Lee, Y., Khazatsky, A., Manning, C. D., Finn, C., (2023). DetectGPT: Zero-shot machine-generated text detection using probability curvature. Proceedings of the 40th International Conference on Machine Learning, 24950-24962.

Nicol, D. J., Macfarlane-Dick, D., (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education 31(2), 199-218. DOI: 10.1080/03075070600572090

Perkins, M., (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice 20(2). DOI: 10.53761/1.20.02.07

Shute, V. J., (2008). Focus on formative feedback. Review of Educational Research 78(1), 153-189. DOI: 10.3102/0034654307313795

Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P., Waddington, L., (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity 19, 26. DOI: 10.1007/s40979-023-00146-z

Zimmerman, B. J., (2002). Becoming a self-regulated learner: An overview. Theory Into Practice 41(2), 64-70. DOI: 10.1207/S15430421TIP4102_2